TECHNICAL EFFICIENCY AND THE TECHNOLOGY...

International Journal of Food and Agricultural Economics

ISSN 2147-8988, E-ISSN: 2149-3766

Vol. 4 No. 2, 2016, pp. 39-50

39

TECHNICAL EFFICIENCY AND THE TECHNOLOGY GAP IN

WETLAND RICE FARMING IN INDONESIA: A METAFRONTIER

ANALYSIS

Mohammad Junaedi

Postgraduate Student, Major of Agricultural Economics (EPN), Faculty of

Economics and Management, Bogor Agricultural University, Jl. Kamper, IPB

Dramaga Campus, Bogor, Indonesia 16680, E-mail: [email protected]

Heny Kuswanti Suwarsinah Daryanto

Department of Agribusiness, Faculty of Economics and Management, Bogor

Agricultural University, Indonesia

Bonar Marulitua Sinaga

Department of Resource and Environmental Economics, Faculty of Economics

and Management, Bogor Agricultural University, Indonesia

Sri Hartoyo

Department of Economics, Faculty of Economics and Management, Bogor

Agricultural University, Indonesia

Abstract

Diversity in the characteristics between regions is why different kinds of technology are

used in wetland rice farming in Indonesia. These differences lead to a technology gap which

then makes it nearly impossible to compare the maximum production sizes (the frontiers) of

each region. This study is aimed to observe the factors that affect production, efficiency, and

the technology gap in wetland rice farming using the data from 4,203 wetland rice farmers

in 4 regions in Indonesia which were obtained from the Cost Structure of Food Crops

Production Survey 2011 conducted by the BPS-Statistics Indonesia. This study was

conducted using a metafrontier analysis to prove that the efficiency level in the 4 regions

could not be compared to each other. In general, all the coefficients of the production

function variables were positive and significant, as expected. The size of the harvested area

was the strongest factor in influencing wetland rice production. The ten socio-economic

variables in this study had various effects on the inefficiency of rice farming in each region.

This study also demonstrated that the use of technical efficiency measurements taken based

on their respective region’s frontier would lead to biased and misguided policies; therefore,

special notes are required in the analysis.

Keywords: Efficiency, metafrontier, technology gap, wetland rice farming

JEL Codes: Q12, Q16, Q18, Y40

1. Introduction

The production process can be judged technically inefficient if it is unable to yield the

maximum productivity, which means that the utilization per unit input bundle does not result

in maximum production (the frontier). The inefficiency issue is suspected to be the reason for

Technical Efficiency And The Technology Gap In Wetland…

40

farmers’ low income and welfare because according to Saptana (2012), a high efficiency

level in agribusiness strongly determines the farmers’ welfare level.

Rice is still the staple food in Indonesia even though there are a few areas in Indonesia

where they consume other staple foods. To this day, the people’s dependence on rice is very

strong; up to 95% of the Indonesian people consume rice as their staple food, even though

Indonesia has 77 foods that have similar or a higher content of carbohydrate than rice.

Because of the rapid population growth and the huge consumption of rice in Indonesia in

addition to the fact that the international rice market is a thin market -rice producing

countries are also rice consumers- Indonesia cannot rely on imported rice alone to fulfill the

domestic demand for rice. Therefore, self-sufficiency policies and food diversification are

important alternatives.

In general, the productivity of wetland rice in Indonesia has increased, even though in the

past few years it has had a tendency to leveling-off. According to Tinaprilla (2012), the

leveling-off of the productivity is thought to be caused by nutrient imbalances in the soil and

the depletion of organic matter in soil as a result of overusing inorganic fertilizers and

pesticides. On the other hand, clearing new farmland is more difficult because the cost for

clearing new paddy fields and rehabilitating irrigation networks is expensive (Tinaprilla,

2012). As long as the food diversification programs do not succeed, the need for a supply of

rice as a staple food is crucial. An important alternative to be considered to ensure the

availability of rice is to increase land productivity through intensification or technology

improvements and increasing rice farming efficiency.

The regions that are rice production centers in Indonesia that need to be considered in the

intensification program are Sumatra, Java, and Bali. In these regions, the challenge to

maintain paddy field use is escalating because it must compete with other agricultural

commodities which are relatively more profitable; moreover, there is shifting in land use as a

result of demands of industrialization and population growth. Meanwhile, regions other than

Sumatra, Java, and Bali such as Kalimantan, Sulawesi and Eastern Indonesia are potential for

extensification programs, but if extensification programs are applied in these regions, it will

also face challenges in the form of high costs of clearing new farmland, establishing

irrigation networks, and also the presence of trade off with other more promising agricultural

commodities and the concern for preserving the forests for carbon trading.

Farmers from different regions, different islands, or different countries will face different

production opportunities. Technically, those farmers would make choices from a number of

different input-output combinations which are also known as a cluster of different kinds of

technology (O’Donnell, Rao & Battese, 2008). Differences in soil fertility, weather

conditions, precipitation, and pest outbreaks among regions would affect farming efficiency

in each region. The economic level, infrastructure and facilities, quality of human resources,

and education level of the farmers which affect technological accessibility and adeptness

would also affect the efficiency of the farming (Chen & Song, 2006). Variation among

regions in the use of input, production techniques, environmental conditions, et cetera are

what Villano, Boshrabadi and Fleming (2010) defined as technology gaps. Interregional

variation caused by indications of a technology gap lead to the fact that the maximum

production size (the frontier) among regions cannot be compared to one another because

each region has its own benchmark. Based on their individual production frontiers, each

region could believe that they have reached a high efficiency level, whereas if compared to

the efficiency level in other regions it might not be efficient yet. This will lead to biased

analysis results and conclusions; therefore, a method that could accommodate the

interregional technology gap, the metafrontier analysis approach, is required (George E.

Battese & Rao, 2002; Chen & Song, 2006; Villano et al., 2010). Due to these conditions and

facts, it is very important to conduct a study of the efficiency of wetland rice farming which

puts interregional comparison in consideration.

M. Junaedi, H. K. S. Daryanto, B. M. Sinaga and S. Hartoyo

41

Many studies about agribusiness efficiency using the metafrontier method have been

conducted in other countries. Among them is the study by Rao, O'Donnell, and Battese

(2003) that studied how to estimate the metafrontier function of the agricultural sector

productivity data among regions and groups of regions between countries using the non-

parametric data envelopment analysis (DEA) and the parametric framework of stochastic

frontier analysis (SFA). Nkamleu, Nyemeck, and Sanogo (2006) studied the agricultural

productivity in the continent of Africa between 1971 and 2000 using the metafrontier

function technique with the purpose to observe the differences in efficiency and the

technology gap in various areas in Africa. The results of the study supported the view that

the technology gap plays an important role in explaining the ability of the agricultural sector

in a certain area to compete with the agricultural sector in numerous other regions in Africa.

The study also has proven that the average technical efficiency of the agricultural sector was

nearly always stable and that there was a marginal decline of productivity potential in the 30-

year period of observation. Boshrabadi, Villano and Fleming (2007) studied the technical

efficiency and the differences in three varieties of pistachios in Iran. In addition to studying

the stochastic frontier production using pooled data and the stochastic frontier production of

each variety, they also studied the stochastic metafrontier production of the three nut

varieties. Using these methods, the technical efficiency score could be corrected using the

variety-technology gap ratio (VTGR). The results demonstrated the importance of

calculating the differences in the frontier production functions set for different nut varieties

because the three methods demonstrated different results. Medhin and Köhlin (2009) used

the stochastic metafrontier approach to investigate the role of small-scale soil conservation in

the highlands of Ethiopia. The estimated stochastic frontier in plot level and the metafrontier

technology gap ratio (TGR) were applied to three soil conservation technology groups and a

group of plots without any soil conservation. The results were that plots with soil

conservation were more technically efficient than plots without conservation. The

metafrontier estimate demonstrated that soil conservation could improve a natural plot’s

technological position.

A study using the metafrontier analysis has been conducted in Indonesia by Tinaprilla

(2012) who studied rice production, technical efficiency and the factors which affected them

and allocation efficiency and rice farming economic efficiency. The study was done using

the 2010 PATANAS data based on the commodity rice in 5 rice-production center provinces

with 592 observations. However, technical efficiency for the metafrontier obtained from this

study was much lower in value than the technical efficiency of the regional frontier

functions; therefore, it was possible that the conclusion and the policy implications made

were biased. In order to assure that the metafrontier estimate functions could encompass the

frontier functions of each region, this study uses the linear programming techniques as used

in the study conducted by O’Donnell et al. (2008). Another point that sets this study apart is

that the division of regions is different and these regions were formed with hypothesis

assessments to ensure the presence of interregional technology gaps.

This metafrontier analysis will resolve a number of issues, including: (1) what factors

affect the production level and technical efficiency of wetland rice farming in the 4 regions

in Indonesia, and what the chances of each region for improving its efficiency are (2) what

the national agribusiness’ maximum technical efficiency potential is, the condition of each

region’s efficiency compared to the national maximum efficiency potential, and what the

chances of each region are in reaching the maximum national potential.

In general, this study aims to analyze the technology gap in wetland rice farming in

Indonesia using the metafrontier production function approach. Specifically, this study aims

to:

1. Identify the factors that affect the production level and analyze the efficiency of

wetland rice farming in Indonesia.


42

2. Measure and analyze the technology gap in wetland rice farming in Indonesia.

2. Study Methodology

2.1. Data Sources and Study Scope

This study uses secondary data, the results of the Cost Structure of Food Crops

Production Survey 2011 conducted by the BPS-Statistics Indonesia. The data analyzed were

obtained from 4,203 respondents, wetland rice farmers from 148 regencies in 13 provinces

which are wetland rice production centers, Aceh, North Sumatra, West Sumatra, South

Sumatra, Lampung, West Java, Central Java, East Java, Banten, Bali, West Nusa Tenggara,

West Kalimantan, and South Sulawesi Provinces. The type of food crop dealt with in this

study was only wetland rice. In line with this study’s aims which are related to the

intensification program, the regions in this study were grouped into Sumatra, Java, Bali and

Other Regions. Pertaining to efficiency and the technology gap in wetland rice farming, the

variables used in this study were: (1) The output in the form of the amount of wetland rice

production; (2) Input: the area harvested, fertilizers, seed, and labor; (3) The characteristics

of the farmers: gender, age, education; (4) The characteristics of the agribusiness: the

planting period (sub-round), the land ownership status, funding, government aid, the use of

soil-tilling equipment; and (5) Institution: expansion, farmer group membership.

The limitation in this study is that the data used is secondary data; therefore, the analysis

conducted was limited to the variables available from the Cost Structure of Food Crops

Production Survey 2011 data. The discussion is limited to the results of the production,

efficiency and technology gap analyses with the metafrontier production function approach.

2.2. Modeling

2.2.1. Stochastic Frontier Production Function

The production function which will be used in this study use is the Cobb-Douglas

production function. There are a number of reasons for using the Cobb-Douglas production

function: its form is relatively simple, it can be transformed into a linear additive form, and it

rarely causes problems. Many previous studies related to the stochastic frontier production

function recommend the use of the Cobb-Douglas production function. This production

function model was initially proposed separately by Aigner, Lovell, and Schmidt (1977) and

Meeusen and van den Broeck (1977). The error term in their model consists of two

components, and as a result, this model was dubbed the "composed error model" by

Jondrow, Lovell, Materov and Schmidt (1982), Abedullah, Kouser and Mushtaq (2007),

Usman, Ilu and Sa’adatu (2013) and other researchers. This model is presented below:

∑ (1)

where yi denotes the output for the ith firm (farmer); Xmi denotes the vector of input m used

by the ith farmer; β denotes the parameter coefficient vector; εi denotes the error term of the

ith farmer.

The error term εi in this stochastic frontier function consists of two elements, vi and ui.

The element vi is an output variation caused by external factors (such as climate, pest

outbreaks, natural disasters, et cetera) which cannot be controlled by the farmer, the

distribution is symmetrical and normal, vi ~ N(0, ζ2

v). Whereas ui reflects the inefficiency

component which is the error term component which is internal (controllable) and is usually

related to the farmer’s managerial capability in managing his/her agribusiness. This


43

component’s distribution is asymmetrical (one sided), ui ≥ 0. If the production process is

efficient (perfect), the output will overlap with the maximum production potential (the

frontier) for the best practice. In this case, there is no inefficiency, which means that ui = 0.

On the other hand, if ui > 0, meaning it is below the potential, it is stated that there is

inefficiency in the agribusiness. The distribution is half normal (u~|N(µi, ζ2

u)|).

Referring to the study by George E. Battese, Rao, and O'Donnell (2004), for a number of

N farmers in a given region who farm wetland rice using an array of inputs, the general form

of the stochastic frontier production function of the ith farmer in the jth region is expressed

by

(2)

with i = 0, 1, 2, ..., and j=1, 2,..., J

The form of equation (2) assumes that the exponents of the frontier production function are

linear in the parameter vector β(j), and xi is a vector (or the transformation) of the ith farmer’s

inputs.

Based on the input and output data of wetland rice farming in the jth region, an estimate

of the frontier production function parameters, both the estimate using the Ordinary Least

Square (OLS) method and the estimate using the Maximum Likelihood Estimation (MLE).

According to Greene (2002), the unbiased estimation method is the MLE. The stochastic

frontier model estimation is conducted through a two phase process. The first phase uses the

OLS method to estimate the parameter for production inputs (βi) and the second uses the

MLE method to estimate the whole production factor parameter (βi), and the variant of the

two error term components vi and ui (ζ2

v and ζ2u).

The general Cobb-Douglas production function using four input variables for the jth

region after being transformed into the linear logarithm can be written as

(3)

where denotes the amount of wetland rice production (tons), denotes the harvest area

(hectare), denotes the amount of labor (man days), denotes the dummy for seed (1-

non-local 0-local), denotes the amount of fertilizer (kg), β0 denotes the intercept, β1, β2,

β3, and β4 are the parameter estimation coefficients, vi – ui denotes the error term (vi is the

random effect, and ui is the non-technical efficiency effect in the model), i denotes the ith

farmer, and j denotes the jth region. The ith farmer’s technical efficiency (TE) in the jth

region (George E Battese & Coelli (1988); O’Donnell et al. (2008)) can be calculated using

(

(4)

The value of technical efficiency is between zero and one, 0 ≤ TEi ≤ 1. Technical efficiency

is the reverse of technical inefficiency; therefore, the value of technical inefficiency is 1-TEi.

The firm’s (the farmer) efficiency is defined as the a farmer’s actual productivity compared

to the maximum potential productivity (Farrell, 1957).

The technical inefficiency function using the ten socio-economic variables deemed to

have an influence on the ith farmer in a given region’s inefficiency in wetland rice farming in

this study can be written as

(

(5)


44

where denotes the technical inefficiency effect, denotes the dummy for the farmers’

gender (1-Male 0-Female), = age (years), = duration of formal education proxied by the

highest diploma owned (0-None/did not graduate from elementary school 6-elementary

school 9-junior high school 12-senior high school 14-D1/D2 15-Academy/D3 17-bachelor

degree D4/S1 20-master/doctorate degree), denotes the dummy for soil tilling (1-Uses a

tractor 0-Does not use a tractor), denotes the dummy for access to credit (1-received credit

0-did not receive credit), denotes the dummy for receiving grants or subsidies (1-yes 0-

no), denotes the dummy for receiving extension (1-yes 0-no), denotes the dummy for

farmer group membership (1-yes 0-no), denotes the dummy for the planting season/sub-

round (1-Rain Season (January to April) 0-Dry Season (May to August)), denotes the

dummy for land ownership status (1-owned by the farmer 0-not owned by the farmer),

denotes the random variable, and , ..., denote the estimate parameters of the

inefficiency variable.

2.2.2. The Metafrontier Production Function and Technology Gap

The term metafrontier was first introduced by George E. Battese and Rao (2002) based

on the study by Hayami and Ruttan (1969) who used the term metaproduction as an envelope

term that includes all existing production functions. George E. Battese and Rao (2002) used

the metafrontier production function to investigate the technical efficiency of firms in

different groups which could possibly have dissimilar technology. There are a number of

approaches that could be used to conduct estimation in relation to frontier production.

However, efficiency estimates in stochastic frontier models usually assume that the

production technology used is the same for all of the farming in every region, while the

different characteristics among regions could lead to the use of different technology in the

regions. The unobserved technology gap is considered inappropriate as an inefficiency factor

if the variations in production technology is not put in consideration (Villano et al., 2010).

This study uses the metafrontier analysis as in George E. Battese et al. (2004) to overcome

the gap in a given region in its agricultural production frontier and to obtain each region’s

comparable technical efficiency. The metafrontier analysis was used not only because of its

ability to conduct estimates of the parameters within the frontier production function and

technical efficiency but also because of its ability in performing an estimation of the

technology gap ratio.

Referring to the study by George E. Battese et al. (2004), the metafrontier production

function model for all the farmers in the 4 regions can be written as

i = 0, 1, 2, ..., N; N = ∑

(6)

where denotes the parameter vector from the metafrontier function so that

, j=1,2,...,j (7)

This metafrontier production function is an envelope function of the stochastic frontier

functions of each region which was developed from the data of all of the farmers in the 4

regions. The metafrontier production function is such a deterministic parametric function that

it has a value of no less than the deterministic components of the stochastic frontier functions

for the regions. The metafrontier production function is a frontier production function which

envelopes all the frontier production functions of each region. The metafrontier function is

assumed to be a smooth function and it envelopes the stochastic frontier functions for

different regions in an unsegmented way (George E. Battese et al., 2004).


45

The stochastic frontier production function of ith farmers in the jth region as in equation

(2) can be expressed in the form of a new equation that involves the metafrontier functions in

equation (6) as follows

(8)

The first form on the right-hand side of equation (8) is the technical efficiency relative to the

stochastic frontier for the jth region as in equation (4), the second form on right-hand side of

equation (8) is the technology gap ratio (TGR) of the ith farmer in the jth region,

(9)

The technology gap ratio (TGR) is defined as the ratio between the highest output which a

region could achieve and the highest output a region could achieve at the metafrontier at the

use of a certain combination of inputs. Because of equation (7), the TGR has a value between

zero and one, 0<TGR<1. If a farmer has a TGR=1, that farmer is already at the point where

he/she is the most efficient in the farming, meaning that that farmer has already used the

input combination and technology which is most optimum, resulting in the most optimum

production and is on the metafrontier. The technical efficiency of the ith farmer relative

towards the potential output at the metafrontier is notated as which is defined as

analogous to equation (4), is the ratio between the output of the ith farmer relative to the

third form on the right-hand side of equation (8) which is the metafrontier output

( 1 )

(10)

Based on the equations (8) to (10), the technical efficiency relative to the metafrontier could

be obtained through the equation

( 2 ) (11)

Because the values of and are between zero and one, the value of is also

between zero and one, 0 < < 1, but smaller than the value.

3. Results and Discussion

Before the analysis is performed, referring to the study by Kokkinou (2012), there needs

to be a hypothesis test to assess whether there is an inefficiency effect on the stochastic

production function of the frontiers in each region, and whether a technology difference

exists in each region. This is necessary because if in all of the regions there is neither the

inefficiency effect nor any technology difference, the technology gap analysis using the

metafrontier analysis is redundant. Based on the results of the analysis which are presented in

Table 1, all the regions could be included in the analysis because the LR values of the test of

the one-sided error are all higher than the χ2 values obtained from Table 1 Kodde and Palm

(1986) at a significance level of α = 5%. Therefore, the zero hypothesis that there are no

inefficiency effects in the stochastic frontier model can be rejected, meaning that there was a

significant inefficiency effect in all the regions. Referring to O’Donnell et al. (2008), the next

hypothesis test is testing whether there is a technology gap between regions. This is tested by

adding up all the log likelihood function ln[L(H1)] values of each region and comparing it to


46

the ln[L(H1)] of the pooled production function of all of the regions. The hypothesis test is

classified as rejecting H0 if ∑ [ ] [ ] . The result is the sum of all

the log likelihood function values of each region (-753.08) is larger than the pooled value of

all regions (-864.33) which means that the zero hypothesis-there is no technology gap in each

region-is rejected. From the two hypothesis tests, it is decided that the technology gap

analysis using the metafrontier approach can be conducted.

Table 1. Regional Production Functions and Inefficiency Region, Pooled and

Metafrontier

Production variable Coe

f.

Sumate

ra

Java Bali Others Pooled Meta (Constant) β0 0.698**

*

0.911*

**

0.834*

**

0.401*

**

0.751*

**

0.8931

Area Planted (X1) β1 0.828**

*

0.875*

**

0.898*

**

0.826*

**

0.858*

**

0.8417 Amount of Labor (X2) β2 0.044**

*

0.064*

**

0.087*

**

0.085*

**

0.061*

**

0.0606

Seed Dummy (X3) β3 0.121**

*

0.086*

**

0.150* 0.026 0.087*

**

0.0167

Amount of Fertilizer

(X4)

β4 0.102**

*

0.058*

**

0.031 0.136*

**

0.081*

**

0.0846

Inefficiency Variable

Gender Dummy (z1) δ1 -0.085 0.075*

**

0.135*

**

0.006 0.052*

*

0.0000 Age(z2) δ2 0.004**

*

0.002*

**

0.004*

**

0.000 0.002*

**

0.0000

Education (z3) δ3 -

0.023**

*

0.000 -

0.014*

**

0.019*

**

-

0.005*

*

0.0000

Tractor Dummy (z4) δ4 -

0.380**

*

-

0.073*

**

-

0.333*

**

-

0.126*

*

-

0.133*

**

0.0000 Credit Dummy (z5) δ5 0.227**

*

-0.015 0.040 -

0.586*

**

0.009 0.0000

Grant Dummy (z6) δ6 -0.010 0.062*

**

-0.045 -

0.176*

**

0.077*

**

0.0000 Expansion Dummy

(z7)

δ7 0.139**

*

-

0.029*

**

-0.007 -

0.177*

**

-

0.038*

*

0.0000

Farmer’s Group

Dummy (z8)

δ8 -

0.232**

*

-

0.066*

**

0.029*

*

0.120*

*

-

0.065*

**

0.0000 Planting Season

Dummy (z9)

δ9 0.058* -0.029* 0.085*

**

0.234*

**

0.015 0.0000

Land Status Dummy

(z10)

δ10 -

0.069**

0.021* -

0.355*

**

-

0.150*

**

-

0.045*

*

0.0000

sigma-squared

0.0949 0.0823 0.0987 0.0959 0.0888 9.37E-

26 gamma

0.1575 0.0008 0.0616 0.1772 0.0096 0.6100

Σβ 0.97 1.00 1.02 1.05 1.00 0.99 log likelihood function -

224.391

7

-316.88 -88.96 -122.86 -864.33 11620

9.4 LR test of the one-

sided error

24.5756 60.817

6

49.137

1

42.322

6

99.437

9

63.482

6 χ2 Kodde & Palm α =

5% 19.0450 19.045

0

19.045

0

19.045

0

19.045

0

Technical Efficiency

(TE) = 0.9571 0.9210 0.9262 0.9280 0.9586 1.0000

TGR 0.9404 0.9187 0.8368 0.8830

TEM

(with

metafrontier) 0.9001 0.8462 0.7748 0.8197

Source: Results of processing

Note: ***=sig. α=1%, **=sig. α=5%, *=sig. α=10%

The results of the data processing presented in Table 1 demonstrate that in general, all the

production function variable coefficients have a positive value and are significant at α=1%,

except in the region of Bali where the dummy coefficient for non-local seed is significant at

α=10% and the variable coefficient of fertilizer is not significant. The area harvested (ha) in

all regions is very dominant in affecting wetland rice production, demonstrated by the

average elasticity which is higher than 80%, compared to the elasticity of the use of labor

and fertilizer. This demonstrates that wetland rice production is quite responsive to the area

harvested, and this condition is not unusual because generally a larger area planted would

increase the wetland rice production; therefore, if the government wishes to make a policy to

increase wetland rice production, one of the main focuses is to increase the area planted.

The results of the data processing also demonstrate that the ten socio-economic variables

have a variety of effects on rice farming inefficiency in each region. In general, the education


47

level, use of tractors, availability of credit, availability of expansion, membership in a

farmers’ group and land ownership status have a negative effect on inefficiency or it could be

interpreted that these factors have a positive effect on the efficiency of wetland rice farming.

On the other hand, the farmer’s gender, age, availability of grants, and planting season

generally have a positive effect on inefficiency, meaning that these factors cause wetland rice

farming to be inefficient.

Table 2. Technical Efficiency and the Technology Gap in Wetland Rice Farming

According to Production Center Regions in Indonesia

Region Number

of Obs. Average Min. Max.

Std.

Dev. Variance

Technical efficiency based on the stochastic frontier production function (TE)

Sumatra 1352 0.9571 0.6382 0.9867 0.0397 0.0016

Java 1788 0.9210 0.7471 1.0000 0.0531 0.0028

Bali 368 0.9262 0.6348 1.0000 0.0923 0.0085

Others 695 0.9280 0.6792 1.0000 0.0559 0.0031

Technology gap (TGR)

Sumatera 1352 0.9404 0.7963 1.0000 0.0367 0.0013

Java 1788 0.9187 0.8240 1.0000 0.0213 0.0005

Bali 368 0.8368 0.6320 0.9868 0.0331 0.0011

Others 695 0.8830 0.7239 1.0000 0.0428 0.0018

Technical efficiency based on the metafrontier production function (TE*)

Sumatera 1352 0.9001 0.5982 0.9700 0.0527 0.0028

Java 1788 0.8462 0.6684 0.9989 0.0531 0.0028

Bali 368 0.7748 0.4578 0.9734 0.0813 0.0066

Others 695 0.8197 0.5625 0.9830 0.0673 0.0045

Analysis of technology gap

TE Rank

TE* Rank

1 Sumatera 0.95707

1 Sumatera 0.90010

2 Others 0.92805

2 Java 0.84624

3 Bali 0.92621

3 Others 0.81975

4 Java 0.92096

4 Bali 0.77482

Source: Results of processing

The technical efficiency (TE) value for each region demonstrates that it could be

considered efficient in each region if the minimum limit is 90%. The highest average

technical efficiency is found in the region of Sumatra region at 95.71% and the lowest is in

the region of Java at 92.10%. The maximum technical efficiency (TE=1) occurs in all

regions except for Sumatra, and the minimum technical efficiency occurs in the region of

Bali. Based on the benchmarks of each region’s frontier, this already-efficient condition has

implications on each region, making each region satisfied with their wetland rice farming

efficiency because the chances to reach a condition of perfect technical efficiency are very

slim.

The technology gap in a certain region against the metafrontier can be measured by

observing the size of the Technology Gap Ratio (TGR). Based on the average TGR, it can be

observed that the region of Sumatra has the smallest technology gap, which means that the

use of technology in Sumatra is relatively better than the other regions. Using the TGR

values, the technical efficiency (TE) of each region could be corrected and compared because


48

the technology gap aspect is considered, leading to new technical efficiency (TE*) values. It

is evident that the technical efficiency value in every region after the technology gap is put in

consideration becomes lower than the technical efficiency value which referred to each

region’s frontier. The implications are that the national agricultural development policies

which are based on the local technical efficiency assessment (without considering the

technology gap aspect) could be biased and misdirected because if the minimum limit is

90%, only the region of Sumatra is efficient. In addition, the region of Sumatra that is

overlooked because it has been thought to be efficient should actually be given special

attention because it is not yet efficient in reality. This means that the regions of Java, Bali

and Others still have a chance to improve their technical efficiency. Based on the ranking of

the TE* values, it can be seen that the region of Java, which was thought to be the most

inefficient, after considering the technology gap aspect, is in the second place after the region

of Sumatra, whereas the region of Bali should receive the most attention in improving its

technical efficiency because in the ranking is in the last position.

4. Conclusion

The size of land harvested has the most dominant influence on wetland rice production in

all the regions in Indonesia, whereas a number of socio-economic variables have a variety of

effects on inefficiency. If the minimum limit used is 90%, in general, based on the size of the

frontier of each region which does not consider the presence of a technology gap, all the

regions in Indonesia are technically efficient; however, if the technology gap is put in

consideration, only the region of Sumatra is relatively efficient. Based on this finding, a

special explanation that the use of the efficiency value cannot be compared with other

regions is needed for the policy implications in a region. For example, the 92.62% efficiency

of wetland rice farming in Bali cannot be proclaimed efficient or more efficient compared

with other regions because this percentage is based on the frontier benchmark for Bali Island

alone. In reality, if the technology gap is put in consideration, the wetland rice farming

agribusiness has an efficiency rate of 77.48% and cannot be considered technically efficient

if the minimum limit is 90%.

The relatively large technology gap against the metafrontier occurs outside of Sumatra

and Java, demonstrating that there exists many opportunities to improve the efficiency of

wetland rice farming in those regions. If the presence of the technology gap aspect is put in

consideration, a priority scale for the wetland rice farming intensification policy could be

developed, starting from the regions that still have many opportunities in decreasing the

technology gap and increasing agribusiness efficiency. Based on the results of this study, the

priority ranking for the regions in Indonesia that need extra attention in the intensification

policy starts with Bali Island, Other regions (aside from Sumatra, Java and Bali), then Java

Island and finally Sumatra Island. Therefore, making the decision based on the technology

gap aspect in creating a priority scale for agricultural development, especially for wetland

rice farming which is based on technical efficiency measures, is more suitable.

References

Abedullah, Kouser, S., & Mushtaq, K. (2007). Analysis of technical efficiency of rice

production in Punjab (Pakistan): implications for future investment strategies. Pakistan

Economic and Social Review, 45(2), 231-244.

Aigner, D. J., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of

stochastic frontier production function models. Journal of Econometrics, 6, 21-37.

Battese, G. E., & Coelli, T. J. (1988). Prediction of firm-level technical efficiencies with a

generalized frontier production function and panel data. Journal of Econometric,

38(1988), 387-399.


49

Battese, G. E., & Rao, D. S. P. (2002). Technology gap, efficiency, and a stochastic

metafrontier function. International Journal of Business and Economics, 1(2), 87-93.

Battese, G. E., Rao, D. S. P., & O'Donnell, C. J. (2004). A metafrontier production function

for estimation of technical efficiencies and technology gaps for firms operating under

different technologies. Journal of Productivity Analysis, 21(1), 91-103.

Boshrabadi, H. M., Villano, R. A., & Fleming, E. (2007). Analysis of technical efficiency and

varietal differences in pistachio production in Iran using a meta-frontier analysis. Paper

presented at the 51st Annual Conference of the Australian Agricultural and Resource

Economics Society, Queenstown New Zealand.

Chen, Z., & Song, S. (2006) Efficiency and technological gap in China's agriculture: a

regional meta-frontier analysis. In U. o. N. (US) (Series Ed.): Vol. 06 (pp. 1-28). Nevada:

University of Nevada.

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal

Statistical Society. Series A (General), 120(3), 253-290.

Greene, W. H. (2002). Econometric Analysis (Fifth Edition ed.). New Jersey (US): Pearson

Education, Inc.

Hayami, Y., & Ruttan, V. W. (1969) Sources of Agricultural Productivity Differences among

Countries Resource Accumulation, Technical Inputs and Human Capital. Vol. P69:

University of Minnesota.

Jondrow, J., Lovell, C. A. K., Materov, I. S., & Schmidt, P. (1982). On the estimation of

technical inefficiency in the stochastic frontier production function model. Journal of

Econometrics, 19 (1982), 233-238.

Kodde, D. A., & Palm, F. C. (1986). Wald criteria for jointly testing equality and inequality

restrictions. Econometrica, 54(5), 1243-1248.

Kokkinou, A. (2012). An industry and country analysis of Technical Efficiency in the

European Union, 1980-2005. (PhD Dissertation), University of Glasgow, Glasgow.

Medhin, H. A., & Köhlin, G. (2009). Soil Conservation and Small-Scale Food Production in

Highland Ethiopia A Stochastic Metafrontier Approach (Vol. 405). Göteborg (SE):

Department of Economics, School of Business, Economics and Law at University of

Gothenburg.

Meeusen, W., & van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas

production functions with composed error. International Economic Review, 18(2), 435-

444.

Nkamleu, G. B., Nyemeck, J., & Sanogo, D. (2006). Metafrontier analysis of technology

gapand productivity difference in African agriculture. Journal of Agriculture and Food

Economics, 1(2), 111-120.

O’Donnell, C. J., Rao, D. S. P., & Battese, G. E. (2008). Metafrontier frameworks for the

study of firm-level efficiencies and technology ratios. Empirical Economics, 34(2008),

231–255. doi: 10.1007/s00181-007-0119-4

Rao, D. S. P., O'Donnell, C. J., & Battese, G. E. (2003). Metafrontier functions for the study

of inter-regional productivity differences (Vol. 01). St. Lucia (AU): School of

Economics, University of Queensland.

Saptana. (2012). Konsep Efisiensi Usahatani Pangan dan Implikasinya Bagi Peningkatan

Produktivitas. Forum Penelitian Agro Ekonomi, 30(2), 109-128.

Tinaprilla, N. (2012). Efisiensi usahatani padi antar wilayah sentra produksi di Indonesia:

pendekatan stochastic metafrontier production function. (PhD Dissertation), Institut

Pertanian Bogor, Bogor (ID).

Usman, S., Ilu, I. Y., & Sa’adatu, B. A. (2013). Improving farmers’ efficiency in rice

production in Nigeria: the relevance of agricultural extension. Journal of Agricultural

Extension, 17(2 (2013 Des)), 159-166.


50

Villano, R., Boshrabadi, H. M., & Fleming, E. (2010). When is metafrontier analysis

appropriate? An example of varietal differences in pistachio production in Iran. J. Agr.

Sci. Tech., 12(2010), 379-389.

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